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Autor:innen: 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
cemmap working paper No. CWP15/14
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically optimal bandwidth that minimizes a higher-order approximation to the asymptotic meansquared error of the estimator of interest. We show that the optimal bandwidth is of the same order as the one minimizing the mean-squared error of the nonparametric plugin estimator, but the constants of proportionality are significantly different. Finally, we develop a data-driven bandwidth selection rule and show, in a simulation experiment, that it may substantially reduce the estimator's mean-squared error relative to existing bandwidth choices, especially when the number of moment conditions is large.
Schlagwörter: 
GMM
higher-order expansion
optimal bandwidth
mean-squared error
long-run variance
JEL: 
C12
C13
C14
C22
C51
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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